Abstract We propose a retrospective cohort study of > 5 million adult members of Kaiser Permanente Northern California during 2000 to 2015, linked to state-of-the-art exposure estimates of fine particulate matter <2.5 ?m in diameter (PM2.5) generated at 1km x 1km resolution using a novel hybrid model that incorporates meteorologic, land-use and satellite measures. By combining these high-resolution PM2.5 exposure estimates with our rich clinical data source in this high-powered study, we will achieve the following Aims. In a cohort of subjects who have no preexisting CVD, we will quantify the associations between ambient PM2.5 exposure and risk of incident CVD events in order to determine whether demographic characteristics (age, sex, race/ethnicity, and SES) and clinical comorbidities (obesity, diabetes, hypertension, and hyperlipidemia) are susceptibility factors that confer elevated risk to the effects of PM2.5. In a cohort of subjects who have a history of CVD, we will quantify the associations between ambient PM2.5 exposure and risk of a subsequent CVD event during follow-up in order to determine whether history of CVD is a susceptibility factor and whether susceptibility varies by particular CVD conditions, and to determine whether demographic characteristics and clinical comorbidities are susceptibility factors that confer elevated risk of the effects of PM2.5 with CVD events. This study provides insights no other cohort or administrative database can address; we are uniquely able to address the question of susceptibility to air pollution because of a convergence of state-of-the-art modeling of PM2.5 exposures at high-resolution, geocoded data, and detailed electronic medical record data that captures time-varying comorbidities, clinical encounters, laboratory and pharmacy data, unavailable in virtually any other population.